plot.svdraws {stochvol} | R Documentation |
Graphical Summary of the Posterior Distribution
Description
plot.svdraws
and plot.svldraws
generate some plots visualizing the posterior
distribution and can also be used to display predictive distributions of
future volatilities.
Usage
## S3 method for class 'svdraws'
plot(
x,
forecast = NULL,
dates = NULL,
show0 = FALSE,
showobs = TRUE,
showprior = TRUE,
forecastlty = NULL,
tcl = -0.4,
mar = c(1.9, 1.9, 1.7, 0.5),
mgp = c(2, 0.6, 0),
simobj = NULL,
newdata = NULL,
...
)
Arguments
x |
|
forecast |
nonnegative integer or object of class |
dates |
vector of length |
show0 |
logical value, indicating whether the initial volatility
|
showobs |
logical value, indicating whether the observations should be
displayed along the x-axis. If many draws have been obtained, the default
( |
showprior |
logical value, indicating whether the prior distribution
should be displayed. The default value is |
forecastlty |
vector of line type values (see
|
tcl |
The length of tick marks as a fraction of the height of a line of
text. See |
mar |
numerical vector of length 4, indicating the plot margins. See
|
mgp |
numerical vector of length 3, indicating the axis and label
positions. See |
simobj |
object of class |
newdata |
corresponds to parameter |
... |
further arguments are passed on to the invoked plotting functions. |
Details
These functions set up the page layout and call volplot
,
paratraceplot
and paradensplot
.
Value
Called for its side effects. Returns argument x
invisibly.
Note
In case you want different quantiles to be plotted, use
updatesummary
on the svdraws
object first. An example
of doing so is given in the Examples section.
Author(s)
Gregor Kastner gregor.kastner@wu.ac.at
See Also
updatesummary
, predict.svdraws
Other plotting:
paradensplot()
,
paratraceplot.svdraws()
,
paratraceplot()
,
plot.svpredict()
,
volplot()
Examples
## Simulate a short and highly persistent SV process
sim <- svsim(100, mu = -10, phi = 0.99, sigma = 0.2)
## Obtain 5000 draws from the sampler (that's not a lot)
draws <- svsample(sim$y, draws = 5000, burnin = 1000,
priormu = c(-10, 1), priorphi = c(20, 1.5), priorsigma = 0.2)
## Plot the latent volatilities and some forecasts
plot(draws, forecast = 10)
## Re-plot with different quantiles
newquants <- c(0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99)
draws <- updatesummary(draws, quantiles = newquants)
plot(draws, forecast = 20, showobs = FALSE,
forecastlty = 3, showprior = FALSE)